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Modeling brain dynamics using computational neurogenetic approach

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Abstract

The paper introduces a novel computational approach to brain dynamics modeling that integrates dynamic gene–protein regulatory networks with a neural network model. Interaction of genes and proteins in neurons affects the dynamics of the whole neural network. Through tuning the gene–protein interaction network and the initial gene/protein expression values, different states of the neural network dynamics can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network of the generation of local field potential. Our approach allows for investigation of how deleted or mutated genes can alter the dynamics of a model neural network. We conclude with the proposal how to extend this approach to model cognitive neurodynamics.

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Acknowledgments

The paper is supported by the Knowledge Engineering and Discovery Research Institute KEDRI (http://www.kedri.info), Auckland University of Technology and the FRST funded grant AUTX02001 (2002–2007). We would like to thank Simei Gomes Wysoski for implementing the CNGM simulator. Alessandro E. P. Villa is gratefully acknowledged for discussions on brain experimental data gathering and processing.

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  1. Lubica Benuskova

    Present address: Department of Computer Science, University of Otago, 90 Union Place East, Dunedin, 9016, New Zealand

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  1. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, AUT Technology Park, 583-585 Great South Road, Penrose, Auckland, 1135, New Zealand

    Nikola Kasabov

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Benuskova, L., Kasabov, N. Modeling brain dynamics using computational neurogenetic approach. Cogn Neurodyn 2, 319–334 (2008). https://doi.org/10.1007/s11571-008-9061-1

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